






"""simple docstring"""





import logging
import os
import sys
from dataclasses import dataclass, field
from importlib import import_module
from typing import Dict, List, Optional, Tuple

import numpy as np
from seqeval.metrics import accuracy_score, fa_score, precision_score, recall_score
from torch import nn
from utils_ner import Split, TokenClassificationDataset, TokenClassificationTask

import transformers
from transformers import (
    AutoConfig,
    AutoModelForTokenClassification,
    AutoTokenizer,
    DataCollatorWithPadding,
    EvalPrediction,
    HfArgumentParser,
    Trainer,
    TrainingArguments,
    set_seed,
)
from transformers.trainer_utils import is_main_process


__lowerCamelCase							=  logging.getLogger(__name__)



@dataclass
class       __A		:
					UpperCAmelCase__								=				field(
					    metadata={"help": "Path to pretrained model or model identifier from huggingface.co/models"}			)
					UpperCAmelCase__								=				field(
					    default=SCREAMING_SNAKE_CASE_     ,metadata={"help": "Pretrained config name or path if not the same as model_name"}			)
					UpperCAmelCase__								=				field(
					    default="NER"     ,metadata={"help": "Task type to fine tune in training (e.g. NER, POS, etc)"}			)
					UpperCAmelCase__								=				field(
					    default=SCREAMING_SNAKE_CASE_     ,metadata={"help": "Pretrained tokenizer name or path if not the same as model_name"}			)
					UpperCAmelCase__								=				field(default=SCREAMING_SNAKE_CASE_     ,metadata={"help": "Set this flag to use fast tokenization."}			)
					# If you want to tweak more attributes on your tokenizer, you should do it in a distinct script,
					# or just modify its tokenizer_config.json.
					UpperCAmelCase__								=				field(
					    default=SCREAMING_SNAKE_CASE_     ,metadata={"help": "Where do you want to store the pretrained models downloaded from huggingface.co"}     ,)



@dataclass
class       __A		:
					UpperCAmelCase__								=				field(
					    metadata={"help": "The input data dir. Should contain the .txt files for a CoNLL-2003-formatted task."}			)
					UpperCAmelCase__								=				field(
					    default=SCREAMING_SNAKE_CASE_     ,metadata={"help": "Path to a file containing all labels. If not specified, CoNLL-2003 labels are used."}     ,)
					UpperCAmelCase__								=				field(
					    default=1_2_8     ,metadata={
					        "help": (
					            "The maximum total input sequence length after tokenization. Sequences longer "
					            "than this will be truncated, sequences shorter will be padded."
					        )
					    }     ,)
					UpperCAmelCase__								=				field(
					    default=SCREAMING_SNAKE_CASE_     ,metadata={"help": "Overwrite the cached training and evaluation sets"}			)



def   a    (     )    ->							Optional[int]:
						# See all possible arguments in src/transformers/training_args.py
						# or by passing the --help flag to this script.
						# We now keep distinct sets of args, for a cleaner separation of concerns.

						__magic_name__:	Optional[Any]							=       HfArgumentParser((ModelArguments, DataTrainingArguments, TrainingArguments)  )
						if len(sys.argv  ) == 2 and sys.argv[1].endswith(""".json"""  ):
												# If we pass only one argument to the script and it's the path to a json file,
												# let's parse it to get our arguments.
												__magic_name__,			__magic_name__,			__magic_name__:	Optional[Any]							=       parser.parse_json_file(json_file=os.path.abspath(sys.argv[1]  )  )
						else:
												__magic_name__,			__magic_name__,			__magic_name__:	Union[str, Any]							=       parser.parse_args_into_dataclasses()

						if (
						    os.path.exists(training_args.output_dir  )
						    and os.listdir(training_args.output_dir  )
						    and training_args.do_train
						    and not training_args.overwrite_output_dir
						):
												raise ValueError(
												    f'Output directory ({training_args.output_dir}) already exists and is not empty. Use'
												    """ --overwrite_output_dir to overcome."""  )

						__magic_name__:	Optional[Any]							=       import_module("""tasks"""  )
						try:
												__magic_name__:	Dict							=       getattr(__UpperCAmelCase      ,   model_args.task_type  )
												__magic_name__:	TokenClassificationTask							=       token_classification_task_clazz()
						except AttributeError:
												raise ValueError(
												    f'Task {model_args.task_type} needs to be defined as a TokenClassificationTask subclass in {module}. '
												    f'Available tasks classes are: {TokenClassificationTask.__subclasses__()}'  )

						# Setup logging
						logging.basicConfig(
						    format="""%(asctime)s - %(levelname)s - %(name)s - %(message)s"""      ,   datefmt="""%m/%d/%Y %H:%M:%S"""      ,   level=logging.INFO if training_args.local_rank in [-1, 0] else logging.WARN      ,   )
						logger.warning(
						    """Process rank: %s, device: %s, n_gpu: %s, distributed training: %s, 16-bits training: %s"""      ,   training_args.local_rank      ,   training_args.device      ,   training_args.n_gpu      ,   bool(training_args.local_rank != -1  )      ,   training_args.fpaa      ,   )
						# Set the verbosity to info of the Transformers logger (on main process only):
						if is_main_process(training_args.local_rank  ):
												transformers.utils.logging.set_verbosity_info()
												transformers.utils.logging.enable_default_handler()
												transformers.utils.logging.enable_explicit_format()
						logger.info("""Training/evaluation parameters %s"""      ,   __UpperCAmelCase  )

						# Set seed
						set_seed(training_args.seed  )

						# Prepare CONLL-2003 task
						__magic_name__:	Optional[int]							=       token_classification_task.get_labels(data_args.labels  )
						__magic_name__:	Dict[int, str]							=       dict(enumerate(__UpperCAmelCase  )  )
						__magic_name__:	int							=       len(__UpperCAmelCase  )

						# Load pretrained model and tokenizer
						#
						# Distributed training:
						# The .from_pretrained methods guarantee that only one local process can concurrently
						# download model & vocab.

						__magic_name__:	List[Any]							=       AutoConfig.from_pretrained(
						    model_args.config_name if model_args.config_name else model_args.model_name_or_path      ,   num_labels=__UpperCAmelCase      ,   idalabel=__UpperCAmelCase      ,   labelaid={label: i for i, label in enumerate(__UpperCAmelCase  )}      ,   cache_dir=model_args.cache_dir      ,   )
						__magic_name__:	Any							=       AutoTokenizer.from_pretrained(
						    model_args.tokenizer_name if model_args.tokenizer_name else model_args.model_name_or_path      ,   cache_dir=model_args.cache_dir      ,   use_fast=model_args.use_fast      ,   )
						__magic_name__:	Union[str, Any]							=       AutoModelForTokenClassification.from_pretrained(
						    model_args.model_name_or_path      ,   from_tf=bool(""".ckpt""" in model_args.model_name_or_path  )      ,   config=__UpperCAmelCase      ,   cache_dir=model_args.cache_dir      ,   )

						# Get datasets
						__magic_name__:	int							=       (
						    TokenClassificationDataset(
						        token_classification_task=__UpperCAmelCase      ,   data_dir=data_args.data_dir      ,   tokenizer=__UpperCAmelCase      ,   labels=__UpperCAmelCase      ,   model_type=config.model_type      ,   max_seq_length=data_args.max_seq_length      ,   overwrite_cache=data_args.overwrite_cache      ,   mode=Split.train      ,   )
						    if training_args.do_train
						    else None
						)
						__magic_name__:	Tuple							=       (
						    TokenClassificationDataset(
						        token_classification_task=__UpperCAmelCase      ,   data_dir=data_args.data_dir      ,   tokenizer=__UpperCAmelCase      ,   labels=__UpperCAmelCase      ,   model_type=config.model_type      ,   max_seq_length=data_args.max_seq_length      ,   overwrite_cache=data_args.overwrite_cache      ,   mode=Split.dev      ,   )
						    if training_args.do_eval
						    else None
						)

						def align_predictions(__UpperCAmelCase      :    np.ndarray      ,   __UpperCAmelCase      :    np.ndarray  ) -> Tuple[List[int], List[int]]:
												__magic_name__:	List[str]							=       np.argmax(__UpperCAmelCase      ,   axis=2  )

												__magic_name__,			__magic_name__:	Union[str, Any]							=       preds.shape

												__magic_name__:	Union[str, Any]							=       [[] for _ in range(__UpperCAmelCase  )]
												__magic_name__:	int							=       [[] for _ in range(__UpperCAmelCase  )]

												for i in range(__UpperCAmelCase  ):
																		for j in range(__UpperCAmelCase  ):
																								if label_ids[i, j] != nn.CrossEntropyLoss().ignore_index:
																														out_label_list[i].append(label_map[label_ids[i][j]]  )
																														preds_list[i].append(label_map[preds[i][j]]  )

												return preds_list, out_label_list

						def compute_metrics(__UpperCAmelCase      :    EvalPrediction  ) -> Dict:
												__magic_name__,			__magic_name__:	int							=       align_predictions(p.predictions      ,   p.label_ids  )
												return {
												    "accuracy_score": accuracy_score(__UpperCAmelCase      ,   __UpperCAmelCase  ),
												    "precision": precision_score(__UpperCAmelCase      ,   __UpperCAmelCase  ),
												    "recall": recall_score(__UpperCAmelCase      ,   __UpperCAmelCase  ),
												    "f1": fa_score(__UpperCAmelCase      ,   __UpperCAmelCase  ),
												}

						# Data collator
						__magic_name__:	Union[str, Any]							=       DataCollatorWithPadding(__UpperCAmelCase      ,   pad_to_multiple_of=8  ) if training_args.fpaa else None

						# Initialize our Trainer
						__magic_name__:	List[str]							=       Trainer(
						    model=__UpperCAmelCase      ,   args=__UpperCAmelCase      ,   train_dataset=__UpperCAmelCase      ,   eval_dataset=__UpperCAmelCase      ,   compute_metrics=__UpperCAmelCase      ,   data_collator=__UpperCAmelCase      ,   )

						# Training
						if training_args.do_train:
												trainer.train(
												    model_path=model_args.model_name_or_path if os.path.isdir(model_args.model_name_or_path  ) else None  )
												trainer.save_model()
												# For convenience, we also re-save the tokenizer to the same directory,
												# so that you can share your model easily on huggingface.co/models =)
												if trainer.is_world_process_zero():
																		tokenizer.save_pretrained(training_args.output_dir  )

    # Evaluation
						__magic_name__:	List[Any]							=       {}
						if training_args.do_eval:
												logger.info("""*** Evaluate ***"""  )

												__magic_name__:	int							=       trainer.evaluate()

												__magic_name__:	int							=       os.path.join(training_args.output_dir      ,   """eval_results.txt"""  )
												if trainer.is_world_process_zero():
																		with open(__UpperCAmelCase      ,   """w"""  ) as writer:
																								logger.info("""***** Eval results *****"""  )
																								for key, value in result.items():
																														logger.info("""  %s = %s"""      ,   __UpperCAmelCase      ,   __UpperCAmelCase  )
																														writer.write("""%s = %s\n""" % (key, value)  )

																		results.update(__UpperCAmelCase  )

    # Predict
						if training_args.do_predict:
												__magic_name__:	Optional[int]							=       TokenClassificationDataset(
												    token_classification_task=__UpperCAmelCase      ,   data_dir=data_args.data_dir      ,   tokenizer=__UpperCAmelCase      ,   labels=__UpperCAmelCase      ,   model_type=config.model_type      ,   max_seq_length=data_args.max_seq_length      ,   overwrite_cache=data_args.overwrite_cache      ,   mode=Split.test      ,   )

												__magic_name__,			__magic_name__,			__magic_name__:	str							=       trainer.predict(__UpperCAmelCase  )
												__magic_name__,			__magic_name__:	Any							=       align_predictions(__UpperCAmelCase      ,   __UpperCAmelCase  )

												__magic_name__:	str							=       os.path.join(training_args.output_dir      ,   """test_results.txt"""  )
												if trainer.is_world_process_zero():
																		with open(__UpperCAmelCase      ,   """w"""  ) as writer:
																								for key, value in metrics.items():
																														logger.info("""  %s = %s"""      ,   __UpperCAmelCase      ,   __UpperCAmelCase  )
																														writer.write("""%s = %s\n""" % (key, value)  )

        # Save predictions
												__magic_name__:	Optional[int]							=       os.path.join(training_args.output_dir      ,   """test_predictions.txt"""  )
												if trainer.is_world_process_zero():
																		with open(__UpperCAmelCase      ,   """w"""  ) as writer:
																								with open(os.path.join(data_args.data_dir      ,   """test.txt"""  )      ,   """r"""  ) as f:
																														token_classification_task.write_predictions_to_file(__UpperCAmelCase      ,   __UpperCAmelCase      ,   __UpperCAmelCase  )

						return results



def   a    (     __UpperCAmelCase      :    Dict  )    ->							Optional[Any]:
						# For xla_spawn (TPUs)
						main()


if __name__ == "__main__":
							main()











"""simple docstring"""





import inspect
import unittest

from transformers import RegNetConfig
from transformers.file_utils import cached_property, is_torch_available, is_vision_available
from transformers.testing_utils import require_torch, require_vision, slow, torch_device

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
  import torch
  from torch import nn

  from transformers import RegNetForImageClassification, RegNetModel
  from transformers.models.regnet.modeling_regnet import REGNET_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
  from PIL import Image

  from transformers import AutoImageProcessor






class 	a		:






    def __init__(			self	:				Union[str, Any]			,		__SCREAMING_SNAKE_CASE	:				str			,		__SCREAMING_SNAKE_CASE	:				List[str]=3			,		__SCREAMING_SNAKE_CASE	:				Optional[int]=32			,		__SCREAMING_SNAKE_CASE	:				int=3			,		__SCREAMING_SNAKE_CASE	:				str=10			,		__SCREAMING_SNAKE_CASE	:				Optional[Any]=[10, 20, 30, 40]			,		__SCREAMING_SNAKE_CASE	:				List[Any]=[1, 1, 2, 1]			,		__SCREAMING_SNAKE_CASE	:				List[str]=True			,		__SCREAMING_SNAKE_CASE	:				Tuple=True			,		__SCREAMING_SNAKE_CASE	:				Optional[int]="relu"			,		__SCREAMING_SNAKE_CASE	:				Tuple=3			,		__SCREAMING_SNAKE_CASE	:				Dict=None			,		)	->						Optional[int]:
      lowerCamelCase_       	=		parent
      lowerCamelCase_       	=		batch_size
      lowerCamelCase_       	=		image_size
      lowerCamelCase_       	=		num_channels
      lowerCamelCase_       	=		embeddings_size
      lowerCamelCase_       	=		hidden_sizes
      lowerCamelCase_       	=		depths
      lowerCamelCase_       	=		is_training
      lowerCamelCase_       	=		use_labels
      lowerCamelCase_       	=		hidden_act
      lowerCamelCase_       	=		num_labels
      lowerCamelCase_       	=		scope
      lowerCamelCase_       	=		len(__SCREAMING_SNAKE_CASE   )






    def   UpperCamelCase			(			self	:				List[Any]   )	->						Dict:
      lowerCamelCase_       	=		floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]   )

      lowerCamelCase_       	=		None
      if self.use_labels:
        lowerCamelCase_       	=		ids_tensor([self.batch_size]			,		self.num_labels   )

      lowerCamelCase_       	=		self.get_config()

      return config, pixel_values, labels






    def   UpperCamelCase			(			self	:				List[str]   )	->						str:
      return RegNetConfig(
          num_channels=self.num_channels			,		embeddings_size=self.embeddings_size			,		hidden_sizes=self.hidden_sizes			,		depths=self.depths			,		hidden_act=self.hidden_act			,		num_labels=self.num_labels			,		)






    def   UpperCamelCase			(			self	:				Optional[Any]			,		__SCREAMING_SNAKE_CASE	:				Union[str, Any]			,		__SCREAMING_SNAKE_CASE	:				List[Any]			,		__SCREAMING_SNAKE_CASE	:				int   )	->						List[Any]:
      lowerCamelCase_       	=		RegNetModel(config=__SCREAMING_SNAKE_CASE   )
      model.to(__SCREAMING_SNAKE_CASE   )
      model.eval()
      lowerCamelCase_       	=		model(__SCREAMING_SNAKE_CASE   )
      # expected last hidden states: B, C, H // 32, W // 32
      self.parent.assertEqual(
          result.last_hidden_state.shape			,		(self.batch_size, self.hidden_sizes[-1], self.image_size // 32, self.image_size // 32)			,		)






    def   UpperCamelCase			(			self	:				List[Any]			,		__SCREAMING_SNAKE_CASE	:				List[Any]			,		__SCREAMING_SNAKE_CASE	:				List[Any]			,		__SCREAMING_SNAKE_CASE	:				List[Any]   )	->						Union[str, Any]:
      lowerCamelCase_       	=		self.num_labels
      lowerCamelCase_       	=		RegNetForImageClassification(__SCREAMING_SNAKE_CASE   )
      model.to(__SCREAMING_SNAKE_CASE   )
      model.eval()
      lowerCamelCase_       	=		model(__SCREAMING_SNAKE_CASE			,		labels=__SCREAMING_SNAKE_CASE   )
      self.parent.assertEqual(result.logits.shape			,		(self.batch_size, self.num_labels)   )






    def   UpperCamelCase			(			self	:				Optional[Any]   )	->						str:
      lowerCamelCase_       	=		self.prepare_config_and_inputs()
      lowerCamelCase_	,      lowerCamelCase_	,      lowerCamelCase_       	=		config_and_inputs
      lowerCamelCase_       	=		{'pixel_values': pixel_values}
      return config, inputs_dict






@require_torch
class 	a		(   __snake_case    , __snake_case    , unittest.TestCase						):
    SCREAMING_SNAKE_CASE		:	int		=					(RegNetModel, RegNetForImageClassification) if is_torch_available() else ()
    SCREAMING_SNAKE_CASE		:	int		=					(
        {"""feature-extraction""": RegNetModel, """image-classification""": RegNetForImageClassification}
        if is_torch_available()
        else {}
    )

    SCREAMING_SNAKE_CASE		:	int		=					False
    SCREAMING_SNAKE_CASE		:	Optional[int]		=					False
    SCREAMING_SNAKE_CASE		:	Optional[int]		=					False
    SCREAMING_SNAKE_CASE		:	int		=					False






    def   UpperCamelCase			(			self	:				Optional[int]   )	->						List[Any]:
      lowerCamelCase_       	=		RegNetModelTester(self   )
      lowerCamelCase_       	=		ConfigTester(self			,		config_class=__SCREAMING_SNAKE_CASE			,		has_text_modality=__SCREAMING_SNAKE_CASE   )






    def   UpperCamelCase			(			self	:				Dict   )	->						Any:
      self.create_and_test_config_common_properties()
      self.config_tester.create_and_test_config_to_json_string()
      self.config_tester.create_and_test_config_to_json_file()
      self.config_tester.create_and_test_config_from_and_save_pretrained()
      self.config_tester.create_and_test_config_with_num_labels()
      self.config_tester.check_config_can_be_init_without_params()
      self.config_tester.check_config_arguments_init()






    def   UpperCamelCase			(			self	:				Any   )	->						Optional[Any]:
      return






    @unittest.skip(reason='RegNet does not use inputs_embeds'   )
    def   UpperCamelCase			(			self	:				List[str]   )	->						Union[str, Any]:
      pass






    @unittest.skip(reason='RegNet does not support input and output embeddings'   )
    def   UpperCamelCase			(			self	:				Any   )	->						Dict:
      pass






    def   UpperCamelCase			(			self	:				Union[str, Any]   )	->						Dict:
      lowerCamelCase_	,      lowerCamelCase_       	=		self.model_tester.prepare_config_and_inputs_for_common()

      for model_class in self.all_model_classes:
        lowerCamelCase_       	=		model_class(__SCREAMING_SNAKE_CASE   )
        lowerCamelCase_       	=		inspect.signature(model.forward   )
        # signature.parameters is an OrderedDict => so arg_names order is deterministic
        lowerCamelCase_       	=		[*signature.parameters.keys()]

        lowerCamelCase_       	=		['pixel_values']
        self.assertListEqual(arg_names[:1]			,		__SCREAMING_SNAKE_CASE   )






    def   UpperCamelCase			(			self	:				Tuple   )	->						List[str]:
      lowerCamelCase_       	=		self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_model(*__SCREAMING_SNAKE_CASE   )






    def   UpperCamelCase			(			self	:				Tuple   )	->						Dict:
      lowerCamelCase_	,      lowerCamelCase_       	=		self.model_tester.prepare_config_and_inputs_for_common()

      for model_class in self.all_model_classes:
        lowerCamelCase_       	=		model_class(config=__SCREAMING_SNAKE_CASE   )
        for name, module in model.named_modules():
          if isinstance(__SCREAMING_SNAKE_CASE			,		(nn.BatchNormad, nn.GroupNorm)   ):
            self.assertTrue(
                torch.all(module.weight == 1   )			,		msg=F'''Parameter {name} of model {model_class} seems not properly initialized'''			,		)
            self.assertTrue(
                torch.all(module.bias == 0   )			,		msg=F'''Parameter {name} of model {model_class} seems not properly initialized'''			,		)






    def   UpperCamelCase			(			self	:				Union[str, Any]   )	->						Optional[Any]:
      def check_hidden_states_output(__SCREAMING_SNAKE_CASE	:				Dict			,		__SCREAMING_SNAKE_CASE	:				Union[str, Any]			,		__SCREAMING_SNAKE_CASE	:				List[Any]   ):
        lowerCamelCase_       	=		model_class(__SCREAMING_SNAKE_CASE   )
        model.to(__SCREAMING_SNAKE_CASE   )
        model.eval()

        with torch.no_grad():
          lowerCamelCase_       	=		model(**self._prepare_for_class(__SCREAMING_SNAKE_CASE			,		__SCREAMING_SNAKE_CASE   )   )

        lowerCamelCase_       	=		outputs.encoder_hidden_states if config.is_encoder_decoder else outputs.hidden_states

        lowerCamelCase_       	=		self.model_tester.num_stages
        self.assertEqual(len(__SCREAMING_SNAKE_CASE   )			,		expected_num_stages + 1   )

        # RegNet's feature maps are of shape (batch_size, num_channels, height, width)
        self.assertListEqual(
            list(hidden_states[0].shape[-2:]   )			,		[self.model_tester.image_size // 2, self.model_tester.image_size // 2]			,		)

      lowerCamelCase_	,      lowerCamelCase_       	=		self.model_tester.prepare_config_and_inputs_for_common()
      lowerCamelCase_       	=		['basic', 'bottleneck']
      for model_class in self.all_model_classes:
        for layer_type in layers_type:
          lowerCamelCase_       	=		layer_type
          lowerCamelCase_       	=		True
          check_hidden_states_output(__SCREAMING_SNAKE_CASE			,		__SCREAMING_SNAKE_CASE			,		__SCREAMING_SNAKE_CASE   )

          # check that output_hidden_states also work using config
          del inputs_dict["output_hidden_states"]
          lowerCamelCase_       	=		True

          check_hidden_states_output(__SCREAMING_SNAKE_CASE			,		__SCREAMING_SNAKE_CASE			,		__SCREAMING_SNAKE_CASE   )






    def   UpperCamelCase			(			self	:				List[Any]   )	->						Tuple:
      lowerCamelCase_       	=		self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_for_image_classification(*__SCREAMING_SNAKE_CASE   )






    @slow
    def   UpperCamelCase			(			self	:				int   )	->						Union[str, Any]:
      for model_name in REGNET_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
        lowerCamelCase_       	=		RegNetModel.from_pretrained(__SCREAMING_SNAKE_CASE   )
        self.assertIsNotNone(__SCREAMING_SNAKE_CASE   )







def 	lowerCamelCase__			(				) ->							Optional[Any]:
  lowerCamelCase_       	=		Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'					)
  return image






@require_torch
@require_vision
class 	a		(   unittest.TestCase						):






    @cached_property
    def   UpperCamelCase			(			self	:				Optional[int]   )	->						Optional[int]:
      return (
          AutoImageProcessor.from_pretrained(REGNET_PRETRAINED_MODEL_ARCHIVE_LIST[0]   )
          if is_vision_available()
          else None
      )






    @slow
    def   UpperCamelCase			(			self	:				Optional[int]   )	->						Dict:
      lowerCamelCase_       	=		RegNetForImageClassification.from_pretrained(REGNET_PRETRAINED_MODEL_ARCHIVE_LIST[0]   ).to(__SCREAMING_SNAKE_CASE   )

      lowerCamelCase_       	=		self.default_image_processor
      lowerCamelCase_       	=		prepare_img()
      lowerCamelCase_       	=		image_processor(images=__SCREAMING_SNAKE_CASE			,		return_tensors='pt'   ).to(__SCREAMING_SNAKE_CASE   )

      # forward pass
      with torch.no_grad():
        lowerCamelCase_       	=		model(**__SCREAMING_SNAKE_CASE   )

      # verify the logits
      lowerCamelCase_       	=		torch.Size((1, 1000)   )
      self.assertEqual(outputs.logits.shape			,		__SCREAMING_SNAKE_CASE   )

      lowerCamelCase_       	=		torch.tensor([-0.4_180, -1.5_051, -3.4_836]   ).to(__SCREAMING_SNAKE_CASE   )

      self.assertTrue(torch.allclose(outputs.logits[0, :3]			,		__SCREAMING_SNAKE_CASE			,		atol=1e-4   )   )




